1. Field of the Invention
This invention is directed to methods and apparatus for registration of medical images of a subject.
2. Description of the Prior Art
The registration of medical images is a useful and sometime necessary precursor to their analysis and review. For example, for treatment monitoring, restaging or follow-up of lung cancer cases, the clinician may have available a number of previous FDG-PET/CT scans with which to compare the current scan. Registration techniques are able to provide the correspondence of locations in such, so-called, longitudinal studies. This correspondence may be used, for example, to link the cross-hairs of a multi-volume display allowing the clinician to compare similar anatomical locations easily and to assess changes therein. Registration of medical volumes may involve rigid or non-rigid (deformable) transformations and single or multiple modalities. Such algorithms operate typically by optimising a similarity function under the constraint of a particular transformation model. For example, for multi-modality deformable registration, gradient descent can be used to optimise a B-spline transformation model under a Mutual Information similarity function, for example as described in “Non-rigid registration of breast MR images using mutual information”, D. Rueckert, C. Hayes, C. Studholme, P. Summers, M. Leach, and D. J. Hawkes, In First Int. Conf. on Medical Image Computing and Computer-Assisted Intervention (MICCAI 1998), Lecture Notes in Computer Science, pages 1144-1152, Cambridge, Mass., 1998. Springer-Verlag.
In certain embodiments, this invention is concerned with deformable registration algorithms. One of the key steps in such algorithms is their initialisation. This is especially crucial if gradient descent is used to optimise the transformation. Where both volumes are of the same patient and are taken across a similar field-of-view (FOV), covering the same area of the body and the amount of deformation is small the initialisation may be achieved simply by aligning the centres of the volumes or alternatively their centres of mass. Hybrid systems (PET/CT, SPECT/CT, MR-PET) rely on mechanical calibration of the joint devices to provide the initial alignment. However, where the FOVs are substantially different and when the images have not been acquired on a hybrid scanner, or in cases where the deformation is large such an approach will not work and the subsequent registration step will fail, as the optimisation algorithm will most probably fall into an irrelevant local minimum.
A number of automated approaches have been proposed to date. One way to perform the initialisation is to do a so-called axis search. Here, the volume with the smaller field of view is translated across the larger volume along the centre of the three axis dimensions x, y and Z. The location at which the similarity function is maximised is chosen as the initial alignment. The methods works in some cases but can give an incorrect result where there is a degree of size difference or rotation between the objects in the images.
A more general approach is to use a low dimensional transformation such as a rigid or affine registration step prior to applying the deformable registration. The method can also work where the degree of initial deformation is small but will fail where degree of change is greater.
An alternative approach is to use a feature based registration algorithm prior to running the main registration approach. Here, a feature detector selects a set keypoints or interest points in both images and a matching algorithm such as RANSAC or robust ICP is used to estimate their correspondence and hence transformation between the interest points. Such techniques can work well but cannot always find a good match when only a small subset of the features are visible in both images and are reliant on the detection of a large number of interest points.
Yet another alternative approach solves the problem by first fitting an anatomical atlas to each image using the anatomical information to initialise the deformable registration algorithm. The key idea is to detect the location of key anatomical features which allows the approximate initial position to be determined in a straightforward and robust manner. For example, in previously considered methods, the centres of the hips, and the base point of the coccyx were used as initial points for alignment. This is in contrast to the generic interest points used previously which detect only features that have particular intensity profiles—edges, corners and so forth. The problem with such an approach is that it relies on the fitting of the anatomical atlas to be perfectly correct. Mislabelled features will cause the algorithm to fail catastrophically.